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Hut101 19 jhwisdom #577

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Hut101 19 jhwisdom #577

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2d18805
feat: add Hugging Face support to ClassifierContainer (#19)
Feb 4, 2026
8cf2c25
feat: fix typo and support Hugging Face models (#192/hut101-#19)
Feb 4, 2026
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22 changes: 21 additions & 1 deletion mapreader/classify/classifier.py
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Original file line number Diff line number Diff line change
Expand Up @@ -108,6 +108,7 @@ def __init__(
is_inception: bool = False,
load_path: str | None = None,
force_device: bool = False,
huggingface=False,
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@rwood-97 rwood-97 Feb 12, 2026

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can you add a type hint here

Suggested change
huggingface=False,
huggingface: bool = False,

**kwargs,
):
# set up device
Expand Down Expand Up @@ -141,6 +142,7 @@ def __init__(
)

self.labels_map = labels_map
self.huggingface = huggingface
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@rwood-97 rwood-97 Feb 12, 2026

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I would skip setting self.huggingface since its only referenced further down in the init and instead just use the huggingface value in the if statement below


# set up model and move to device
print("[INFO] Initializing model.")
Expand All @@ -149,7 +151,25 @@ def __init__(
self.input_size = input_size
self.is_inception = is_inception
elif isinstance(model, str):
self._initialize_model(model, **kwargs)
if self.huggingface:
try:
from transformers import AutoModelForImageClassification, AutoImageProcessor
except ImportError:
raise ImportError(
"Hugging Face models require the 'transformers' library: 'pip install transformers'."
)
print(f"[INFO] Initializing Hugging Face model: {model}")
num_labels = len(self.labels_map)
self.model = AutoModelForImageClassification.from_pretrained(
model,
num_labels=num_labels,
ignore_mismatched_sizes=True
).to(self.device)
self.hf_processor = AutoImageProcessor.from_pretrained(model)
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@rwood-97 rwood-97 Feb 12, 2026

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I think this could also be just hf_processor instead of an attribute self.hf_processor since it isn't used outside of this function

self.input_size = getattr(self.hf_processor, "size", {"height": 224})["height"]
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@rwood-97 rwood-97 Feb 12, 2026

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Looking here there seem to be 3 options for how size is defined.

Could you implement these, i.e.:

Suggested change
self.input_size = getattr(self.hf_processor, "size", {"height": 224})["height"]
size = getattr(hf_processor, "size", {})
if "height" in size and "width" in size:
self.input_size = (size["height"], size["width"])
elif "shortest_edge" in size:
self.input_size = (size["shortest_edge"], size["shortest_edge"])
else:
self.input_size = input_size

self.is_inception = False
else:
self._initialize_model(model, **kwargs)

self.optimizer = None
self.scheduler = None
Expand Down
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